“Too many Americans suffer from foodborne illnesses every year. Making the food supply more digitally enabled and food more traceable will speed the response to outbreaks and deepen our understanding of what causes them and how to prevent them from happening again,” said Acting FDA Commissioner Janet Woodcock, M.D. in an agency release “One of the FDA’s highest priorities is protecting consumers from foodborne illnesses. We hope to find new, innovative ways to encourage firms of all sizes to voluntarily adopt tracing technologies that can help our nation modernize the way we work together to determine possible sources of foodborne illnesses as quickly as possible to keep Americans safe.”
Additional information about the challenge, which ends on July 30, can be found on the precisionFDA website.
The FDA is asking for $6.5 billion, about an 8% increase over the previous year, for its FY 2022 budget. The budget includes a $185 investment in the agency’s critical public health infrastructure, which addresses enterprise-wide data modernization and enhanced technology to ensure that labs and facilities are safe and integrated with program needs, and capacity building. FDA is asking for $97 million to increase the development of its food and medical product safety programs. Specific areas of investment within food safety include boosting funding given to programs that address maternal and infant nutrition ($18 million); providing funds that tackle emerging food-related chemical and toxicological issues ($19.7 million); and improving the oversight of animal foods and supporting the implementation of the New Era of Smarter Food Safety Blueprint ($22 million). Accompanying FDA’s budget request are legislative proposals to enhance the agency’s authorities to protect and promote public health.
FSMA-Based & Technology-Enabled: FDA Advances into New Era of Smarter Food Safety, a special keynote with Frank Yiannas, FDA
Digital Transformation in Food Safety, with Natasa Matyasova and Matt Dofoo, Nestlé
Consumer-focused Food Safety, with Mitzi Baum, STOP Foodborne Illness
TechTalks from Controlant, Veeva and Primority
This year’s event occurs as a Spring program and a Fall program. Haven’t registered? Follow this link to the 2021 Food Safety Consortium Virtual Conference Series, which provides access to all the episodes featuring critical industry insights from leading subject matter experts! Registration includes access to both the Spring and the Fall events. We look forward to your joining us virtually.
In today’s digital-first world, it might be surprising for those outside of the food manufacturing industry to learn that paper and pen are still considered state-of-the-art documentation tools. Answering food safety and quality questions such as: “What was the underlying cause of this customer complaint?” or “What caused the production halt this morning?” still require hours of research across paper documents, emails and spreadsheets. Maybe even the odd phone call or text message.
The good news is that many food safety and quality problems can be solved by leveraging modern-day technology. The challenge is taking that first step. By applying the following best practices, organizations can take small steps that lead to substantial benefits, including optimized food safety and quality programs, happier employees and safer operations.
Digital Transformation Best Practices
What if all the information food safety professionals require could be accessible through one unified interface and could proactively point to actions that should be taken? It can, with the right mindset and the right strategy.
While there is no “flip of a switch” to become digitally empowered, best practices exist for where to start. And, early adopters are injecting innovation into food safety programs with simple, but powerful technology.
Too often, food safety professionals push forward on a path to digital transformation by evaluating software and business applications against features and/or cost. But before taking this approach, it is important to look at existing food safety programs, identify where incremental improvements can be made and determine the potential return on a new technology investment.
Self-awareness is a beneficial leadership skill, but it’s also the key driver in understanding an organization’s business needs for food safety. Food safety professionals need to get real about common pain points, such as inconsistent or insufficient data, non-standardized practices, and delayed reporting. This is not the time to gloss over problems with processes or tools. Only by clearly documenting the challenges upfront will organizations be able to find the best solutions.
As one example, a common pain point is managing different formats and timing of reporting across facilities. See if this sounds familiar: “Well, Dallas sends an Excel spreadsheet every week, but Toledo only sends it on a monthly basis, while Wichita sends it monthly most of the time, but it’s never in the same format.”
Start out by identifying similar problems to help define the business objective, which will help determine how technology can be most effectively applied.
Eat, Sleep, Food Safety, Repeat
Food safety processes should constantly evolve to enable continued improvements in food safety outcomes. With that in mind, it’s helpful to dust off the corporate SOP and review it, especially if an organization is moving to a digital program. A common mistake many food manufacturers make is asking technology providers to configure an application based solely off the corporate protocol, only to discover at go-live that users don’t follow that protocol.
To avoid this situation, consider the following questions:
Why are food safety professionals not completing processes by the book?
Is that similar with every site?
Why has it been that way for so long?
Why did food safety professionals start to stray?
By locking down processes and identifying the desired way forward, leaders can configure a new application with the latest information and updated decisions. At a minimum, this step will help identify current issues that should be addressed, which can become measurable goals for the use of the new technology, ideally emphasizing the most pressing problems.
Less is More
Digital transformation doesn’t always need to become a “fix-all” project. Instead, it may revolve around a single operational initiative or business decision. For example, food safety professionals often maintain a spreadsheet with usernames and passwords for countless applications, some of which overlap in functionality and/or require a separate login for each facility. This is not only a safety concern, it’s an easy entry point when moving to a digital approach.
Consolidation of applications is a natural step from the standpoint of feasibility and fiscal responsibility. So, look for digital transformation opportunities that result in fewer applications and more consolidation.
Don’t Rush It
While digital transformation is inevitable, Rome wasn’t built in a day and neither should be an organization’s digital strategy. Unfortunately, the decision to go digital is often made, and a go-live date chosen, before determining what transformation requires, which is a clear-cut recipe for failure.
Technical vendors should play a key role in developing an effective implementation strategy, including sharing onboarding, planning, configuration and go-live best practices.
While technology is here to help the world become smarter about food safety, it is not here to replace human experience. Food safety leaders should continue to augment processes through supplemental technologies, rather than view technology as a full takeover of current approaches.
Barriers to entry for digital transformation are being lowered, as the ease of adoption of the underlying technologies continues to advance and access via cloud-based applications improves.
What to Do With All This Data? 5 Outcomes Food Manufacturers Can Achieve
Food manufacturers have benefited from digitally transforming environmental monitoring programs (EMPs) using workflow and analytics tools in a variety of ways. In the end, what matters is that the resulting data access and usability enables new insights and accelerates decisions that result in reduced risk and improved quality. Keep in mind these key outcomes that food manufacturers can achieve from digital transformation.
Enhancing an internal audit framework with digital tools will greatly reduce the burden of ensuring compliance for schemes such as BRC, SQF and FSSC food safety standards. Flexible report formats and filtering capabilities empower users with the right information at the right time.
Imagine, no more sifting manually through binders of CoA’s and test records to find a needle in a haystack. Exposing teams to a digital means of performing internal audits will not only boost confidence to handle requests from an auditor but will also help drive continuous improvement by providing easier access to insights about the effectiveness of internal policies. At the same time, digital tools will help ensure that only the required information is shared, reducing confusion and uncertainty as well as audit time and cost.
Outcome #2: Proactive Alerting and Automated Reporting
Threshold-based report alerts are an excellent way to reduce the noise often associated with notification systems. Providing quality and safety managers with automated alerts of scheduled maintenance or pending test counts can help them focus on activities that need attention, without distractions.
The benefit of threshold-based reporting is that it is a “set it and forget it” method. While regular “Monday Reports” are still a necessity, alerts and reports can be generated only when attention is needed for anomalies. A great example of this is being able to set proactive alerts for test counts in a facility that are approaching nonconformance levels. Understanding the corrective action requirements needed to control an environmental issue before it impacts quality, production and unplanned sanitation measures is a critical component of risk management and brand protection. In addition, reports can be automatically generated and delivered on a regular schedule to help meet reporting needs without spending time collecting data.
In other words—imagine a world where data comes and finds users when needed, rather than having to search for it in a binder or spreadsheet. Digital tools can provide email reports showing that a threshold has (or has not) been met and link the user directly to the information needed to take action. This is called “actionable information” and is something to consider when deploying technology within an organization’s food safety program processes.
Outcome #3: Optimize Performance with Tracking, Trending and Drilling
The Pareto Principle specifies that for many outcomes, about 80% of consequences come from 20% of causes. Historical data that is digitized can be used to quickly identify the root cause of top failures in a facility in order to drive process improvements. Knowing where to invest money will help avoid the cost of failure and aid in the prevention of a recall situation.
Dashboards are a powerful tool that organizations can use to understand the risk level across facilities to make better, data-driven decisions. Reports can be configured through a thoughtful dashboard setup that enables users to easily identify hot spots and trends, drill down to specific test locations, and enable clear communication to stakeholders. Figure 1 provides an example of a heat map that can be used to speed response and take corrective actions when needed.
Outcome #4: Simplified Data Governance and Interoperability
Smarter food safety will drive standardization of data formats, which allows information to flow seamlessly between internal and external systems. One of the major benefits of shifting away from paper-based solutions is the ability to be proactive to reduce risk and cost. FSQA managers, within and across facilities, can benefit from a 360-degree operational view that reveals hidden connections between information silos that exist in the plant and across the organization. This includes:
Product tracing through product testing to environment monitoring and sanitation efforts
Tracing back a product quality issue reported from a customer to the sanitation efforts
Understanding why compliance is on track but quality results aren’t correcting
Outcome #5: Reduce the Cost of High Turnover
Successful GMPs, SSOPs and a HACCP program require leaders that continually ensure that employees are properly trained, which can be difficult with high turnover rates. To address this challenge, digital tools can aid in providing easily accessible documentation to empower users and reduce the cost, time and risk associated with having to re-train new employees on the EMP process. While training cannot be replaced with technology, it can be accelerated.
For example, testing locations within facilities can be documented with images and related information enabling new employees to visually see the floorplan and relevant testing protocols with accompanying video and click-through visualization of underlying data. Additionally, corrective action protocols can be enhanced with videos and standardized form inputs to ensure proper data is being collected at all times.
The Path Ahead
As the digital transformation of the food safety industry continues, food manufacturers should seek out and apply proven best practices to make the process as efficient and effective for their organization as possible. By avoiding common pitfalls, companies can achieve transformation objectives and realize substantial benefits from more easily accessible and actionable food safety data.
The COVID-19 pandemic heightened the urgency for food brands to adopt technology solutions that support remote management of environmental monitoring programs (EMPs) as they strive to provide safe products to customers. While digital transformation has progressed within the food safety industry, food and beverage manufacturers often have lower profitability as compared to other manufacturing industries, such as pharmaceutical and high-tech equipment, which can lead to smaller IT spend.1 Many companies still rely on manual processes for environmental monitoring and reporting, which are prone to error, fail to provide organizations with visibility into all of their facilities and limit the ability to quickly take corrective actions.
Despite growing recognition of the value of automating testing, diagnostics, corrective actions and analytic workflows to prevent contamination issues in food production environments, barriers to adoption persist. One key obstacle is the recurring mindset that food safety is a necessary compliance cost. Instead, we need to recognize that EMP workflow automation can create real business value. While the downside of food safety issues is easy to quantify, organizations still struggle to understand the upside, such as positive contributions to productivity and a stronger bottom-line achieved by automating certain food safety processes.
To understand how organizations are using workflow automation and analytics to drive quantifiable business ROI, a two-year study that included interviews and anonymized data collection with food safety, operations, and executive leadership at 34 food organizations was conducted.
The respondents represent more than 120 facilities using advanced EMP workflow automation and analytics. Based on the interviews and the shared experience of food organization leaders, two key examples emerged that demonstrate the ROI of EMP automation.
Improved Production Performance
According to those interviewed, one of the primary benefits of EMP automation (and driver of ROI) is minimizing production disruptions. A temporary conveyor shutdown, unplanned cleaning, or extensive investigatory testing can add up to an astounding 500 hours annually at a multi-facility organization, and cost on average $20,000 to $30,000 per hour.2 So, it’s obvious that eliminating costly disruptions and downtime has a direct impact on ROI from this perspective.
But organizations with systems where information collected through the EMP is highly accessible have another advantage. They are able to take corrective actions to reduce production impacts very quickly. In some cases, even before a disruption happens.
By automatically feeding EMP data into an analytics program, organizations can rapidly detect the root cause of issues and implement corrective actions BEFORE issues cause production delays or shutdowns.
In one example, over the course of several months, a large dairy company with manual EMP processes automated its food safety workflows, improved efficiencies, reduced pathogen positives and improved its bottom line. At the start of the study, the company increased systematic pathogen testing schedules to identify where issues existed and understand the effectiveness of current sanitation efforts. With improved access to data on testing, test types and correlated sanitation procedures, the company was able to implement a revamped remediation program with more effective corrective action steps.
Ultimately, the automated workflows and analytics led to reduced positive results and more efficient EMP operations for the company as compared to the “crisis-mode” approach of the past. The associated costs of waste, rework, delayed production starts, and downtime caused by food safety issues were significantly reduced as illustrated in Figure 1.
Quantifying the ROI of Production Performance Improvements
The financial impact of reducing production downtime by just 90 minutes per week can be dramatic when looked at by cumulative results over multiple weeks. In fact, eliminating just a few delayed starts or unplanned re-cleaning can have significant financial gains.
Figure 2 shows the business impact of gaining 90 minutes of production up-time per week by automating food safety operations. For the purposes of this analysis, the “sample organization” depicted operates two facilities where there are assumptions that down-time equates to a cost value of $30,000 per hour, and that both plants experience an average of 90 minutes of downtime per week that can be re-gained.
A key challenge shared by study participants was detecting food safety issues early enough to avoid wasting an entire production run. Clearly, the later in a processing or manufacturing run that issues are discovered, the greater the potential waste. To limit this, organizations needed near real-time visibility into relevant food safety and EMP data.
By automating EMP workflows, they solved this issue and created value. By tracking and analyzing data in near real time, production teams were able to keep up with ever-moving production schedules. They could define rules to trigger the system to automatically analyze diagnostic results data and alert stakeholders to outliers. Impacted food product could be quickly identified and quarantined when needed before an entire production run was wasted.
Companies included in the study realized substantial benefits from the increased efficiencies in their testing program. According to a food safety quality assurance manager at a large U.S. protein manufacturer, “Our environmental monitoring program has reached new heights in terms of accuracy, communication, visibility and efficiency. Manual, time-intensive tasks have been automated and optimized, such as the ability to search individual sample or submittal IDs, locate them quickly and make any necessary changes.”
Quantifying the ROI of Food Waste Reductions
Figure 3 shows how measuring the business impact of gaining back just 10% of scrapped food per week. For the purposes of this analysis, the “sample organization” depicted operates two facilities where there are 500 lbs. of finished product scrapped each week, and the value per pound of finished product is valued at a cost of $1 per pound.
Automating EMP workflows decreases the time required to receive and analyze critical EMP data, helping food manufacturers achieve significant improvements in production performance, waste reduction and overall testing efficiency. By using these same ROI calculations, food brands can better illustrate how improved food safety processes can build value, and help leaders see food safety as a brand imperative rather than a cost center. As food organizations progress through each stage of digital transformation, studies like this can show real-world examples of business challenges and how other organizations uncovered value in adoption of new technologies and tools.
Last year’s annual GFSI Conference was held in Seattle just weeks before the World Health Organization (WHO) declared COVID-19 a pandemic. This year’s event looked very different, as it joined the virtual event circuit—with hundreds of attendees gathering from across the globe, but from the comfort of their homes and offices. The 2021 GFSI Conference reflected on lessons learned over the past year, the fundamentals of building a better food system, and the idea that food safety is a collaborative effort that also encompasses training programs, effectively leveraging data and capacity building.
The pandemic provided the opportunity to reimagine safer, more resilient and sustainable food systems, said Dr. Naoki Yamamoto, universal health coverage, assistant director-general, UHC, Healthier populations at WHO. She also offered three clear messages that came out of the pandemic:
Food safety is a public health priority and a basic human right. Safe food is not a luxury.
Food safety is a shared responsibility. Everyone in the food chain must understand this responsibility and work towards a common goal.
Good public private partnership can bring new opportunities and innovative solutions for food safety. We need to seek more collaborative approaches when working across sectors to achieve foods safety.
During the session “Ready for Anything: How Resiliency and Technology Will Build Consumer Trust and Help Us Mitigate Disruption in the 21st Century”, industry leaders discussed how the pandemic reminded us that a crisis can come in many forms, and how applying the right strategy and technology can help us remain resilient and equipped to address the challenges, said Erica Sheward, GFSI director.
“When you think about business resiliency—it’s about our own, but most importantly, it’s about helping our customers become more resilient to those disruptions,” said Christophe Beck, president and CEO of Ecolab. He added that being able to predict disruptions, help customers respond to those disruptions, and provide real-time control to learn and prepare for the next pandemic or serious crisis is critical. Companies need to ensure their technology systems and contingency plans are ready to go, advised David Maclennan, chairman and CEO of Cargill. The key to a resilient food supply chain system is access and the ability to keep food moving across borders. And above all, whether dealing with a health crisis or a food safety crisis, consumers must always be front and center, said Natasa Matyasova, head of quality management at Nestle. “In short term, [it’s] first people, then business contingency, and then help the community as needed,” she said.
It is an exciting time to be in the food industry. Consumers are ever more aware of what they are eating and more demanding of quality. And the vital need to reduce global food waste is transforming how we produce and consume food. This is driving innovation all the way along the supply chain, from gate to plate.
One of the biggest areas of opportunity for the industry to increase automation and improve food safety is in the processing plant. The challenges processors have faced in the last 12 months have accelerated the focus on optimizing resources and the drive for more adoption of new technology.
Foreign material contamination is a growing issue in the meat industry and new types of detection systems are emerging to help address this challenge. As Casey Gallimore, director of regulatory and scientific affairs at the North American Meat Institute, highlighted in a recent webinar, 2019 was a record year for the number of recalls related to foreign object contamination, which totaled 27% of all FSIS recalls in that year.
“There are a number of potential reasons why recalls due to foreign object contamination have increased over the years: Greater regulatory focus, more discerning consumers, [and] more automation in plants. But one important reason for this trend is that we have a lot of new technology to help detect more, [but] we are not necessarily using it to its full potential,” said Gallimore. “As an industry, we have a strong track record of working together to provide industry-wide solutions to industry-wide problems. And I believe that education is key to understanding how different detection systems—often used together—can increase the safety and quality of our food.”
Types of Detection Systems
Processors use many different detection systems to find foreign materials in their products. Equipment such as x-rays and metal detectors, which have been used for many years, are not effective against many of today’s contaminants: Plastics, rubber, cardboard and glass. And even the most well trained inspectors are affected by fatigue, distraction, discomfort and many other factors. A multi-hurdle approach is imperative, and new technologies like vision systems need to be considered.
Vision systems, such as cameras, multi-spectral, and hyperspectral imaging systems can find objects, such as low-density plastics, that may have been missed by other detection methods. Yet, depending on the system, their performance and capabilities can vary widely.
Camera-based systems are the most similar to the human eye. These systems are good for distinguishing objects of varying size and shape, albeit in two-dimensions rather than three. But they become less effective in situations with low contrast between the background and the object being detected. Clear plastics are a good example of this.
Multi-spectral systems are able to see more colors, including wavelengths outside of the visible spectrum. However, multispectral systems are set up to use only specific wavelengths, which are selected based on the materials that the system is expected to detect. That means that multispectral systems can identify some chemical as well as visual properties of materials, based on those specific wavelengths. It also means that other materials, which the system has not been designed to find, will likely not be detected by a multispectral system.
Another relatively new type of vision system uses hyperspectral imaging. These systems use chemistry to detect differences in the materials being inspected and therefore recognize a broad range of different contaminants. They are especially good at seeing objects that cameras or human inspectors may miss and at identifying the specific contaminant that’s been detected. The same system can assess quality metrics such as composition and identify product flaws such as woody breast in chicken. Hyperspectral systems also gather tremendous amounts of chemistry data about the products they are monitoring and can use artificial intelligence and machine learning to get a more holistic picture of what is happening in the plant over time, and how to prevent future contamination issues. This might include identifying issues with a specific supplier, training or other process challenges on one line (or in one shift), or machinery in the plant that is causing ongoing contamination problems.
Many processors are considering implementing new inspection systems, and are struggling to understand how to compare the expected performance of different systems. One relatively simple methodology that can be used to evaluate system performance is, despite its simplicity, called a “Confusion Matrix”.
The Confusion Matrix
A confusion matrix is often used in machine learning. It compares the expected outcome of an event with the actual outcome in order to understand the reliability of a test.
Figure 1 shows four possible outcomes for any kind of test.
Actual (True Condition)
True Positives (TP)
False Positives (FP)
False Negatives (FN)
True Negatives (TN)
P = TP + FN
N = FP + TN
Figure 1. Confusion Matrix
But what does a confusion matrix tell us, and how can it help us assess a detection system?
The matrix shows us that a detection system may incorrectly register a positive or negative detection event—known as a ‘False Positive’ or ‘False Negative’.
As an example, say we are testing for a disease such as COVID-19. We want to know how often our system will give us a True Positive (detecting COVID when it *IS* present) versus a False Positive (detecting COVID when it *IS NOT* present).
Let’s apply this to processing. If you are using an x-ray to detect foreign objects, a small piece of plastic or wood would pass through unnoticed. This is a False Negative. By contrast, a system that uses hyperspectral imaging would easily identify that same piece of plastic or wood, because it has a different chemical signature from the product you’re processing. This is a True Positive.
A high rate of false negatives—failing to identify existing foreign materials—can mean contaminated product ends up in the hands of consumers.
The other side of the coin is false positives, meaning that the detector believes foreign material to be present when in fact it is not. A high rate of False Positives can lead to significant and unnecessary product wastage, or in time lost investigating an incident that didn’t actually occur (see Figure 2).
The secret to a good detection system lies in carefully balancing the rates of true positives and false positives by adjusting the sensitivity of a system.
This is where testing comes in. By adjusting a system and testing under different conditions, and then plotting these outcomes on the confusion matrix, you get an accurate picture of the system’s performance.
Effectiveness of a Detector
Detection is not just the act of seeing. It is the act of making a decision based on what you have seen, by understanding whether something of importance has occurred. Many factors influence the effectiveness of any detection system.
Resolution. This is the smallest size of object that can possibly be detected. For example, when you look at a photograph, the resolution affects how closely you can zoom in on an image before it becomes blurry.
Signal to noise ratio. This measures the electronic “noise” of the detector and compares it with the “background noise” that may interfere with the signals received by the detector. Too much background noise makes it harder to identify a foreign object.
Speed of acquisition. This measures how fast the detector can process the signals it receives. Motion limits what you can see. As line speeds increase, this impacts what detectors are able to pick up.
Material being detected. The type of material being detected and its properties will have a significant impact on the likelihood of detection. As previously mentioned, for example, x-rays are unlikely to detect low-density materials such as cardboard, resulting in a high number of False Negatives.
Presentation or location of material being detected. Materials that are underneath another object, that are presented on an angle, are too similar to the product being inspected, or are partially obstructed may be more difficult for some detectors to find. This also presents a risk of False Negatives.
Complexity of the product under inspection. Product composition and appearance vary. For example, just like the human eye, finding a small object on a uniformly illuminated and uniform color background like a white kitchen floor is much easier than finding the same small object on a complex background like industrial carpet. Coarsely ground meat might be more difficult to detect than uniform back fat layers, for example.
Environment. Conditions such as temperature and humidity will have a significant effect on detection.
To understand system performance even better, we can use a detection curve. This plots out the likelihood of detection against different variables (e.g., object size) and allows us to objectively compare how these different factors impact the performance of each system.
Figure 3 shows how this looks when plotted as a curve, with object size on the x-axis (horizontal) and the probability of detection (a True Positive from the Confusion Matrix) on the y-axis (vertical). It shows three examples of possible detection curves, depending on the detector being used.
A detection curve tells you both the smallest and largest object that a detector will find and the probability that it will be found.
In the example presented by Figure 3, Detector 3 can see essentially 100% of large and very large objects, as can Detector 2. But Detector 3 is also more likely than the other two systems in the example to see microscopic objects. Based on this detection curve it would likely be the best option if the goal were to detect as many foreign objects as possible, of all sizes.
Of course, the performance of a detector is determined by multiple measures, not just size,
Detection capability can be improved for most detection systems, but typically comes at a significant cost: Increasing sensitivity will increase the number of false positives, resulting in increased product rejection. This is why looking at the detection curve together with the false-positive/false-negative rates for any detection system gives us a clear picture of its performance and is invaluable for food processing plants when selecting a system.
Using the confusion matrix and a detection curve, processors can compare different detection systems on an apples-to-apples basis. They can easily see whether a system can identify small, tiny or microscopic objects and, crucially, how often it will identify them.
Every detection method—X -ray, metal detection, vision systems, manual inspection—presents a trade-off between actual (correct) detection, rejection of good product (false positive) and missed detections (false negative). This simple way to compare differences means processors can make the right decision for the specific needs of their plant, based on easily gathered information. For all of us data geeks out there, that sounds like the Holy Grail.
The COVID-19 pandemic has brought challenges to all industries, and many restaurants have been forced to close their doors permanently. Restaurant owners have struggled due to COVID-19 restrictions that have drastically cut the number of customers they can serve—whether as a result of an indoor dining ban or capacity limits. Those that have been allowed to re-open are being stretched to meet new guidelines to keep guests safe and comfortable while dining. Not only do restaurant owners need to make sure their restaurants are COVID-safe, but they also need to ensure they are providing the quality service and meals their customers have come to know and love. The Internet of Things (IoT) can not only ease the burden of implementing new protocols while also ensuring a clean and safe environment for both employees and patrons, but also help restaurants enhance efficiency.
The following are some points on how the IoT can help restaurants not only survive, but thrive amid the pandemic.
Easy-to-deploy IoT-enabled devices provide several benefits to QSRs, including the monitoring of employee hand washing stations, dishwashing water temperatures, sanitizer solution concentrations and customer bathroom usage frequency to ensure constant compliance with cleanliness standards.
By placing sensors on tables and work lines, restaurant owners can collect valuable data and insights in real time. For example, the sensors can share information about how often tables are being cleaned. This information will help owners trust that tables are being cleaned thoroughly in between each use.
Sensors can also be placed on washbasins to monitor employee hand washing. Sensors on the sinks will not only confirm that employees’ hands have been washed, but they will also share exactly how long employees washed their hands. That way, owners can have peace of mind knowing employees’ hands and restaurant surfaces are properly sanitized before customers sit down to eat. With door sensors monitoring customer bathrooms, store owners can ensure adequate cleaning is allocated based on frequency of usage.
Owners can also have peace of mind knowing their restaurant is rodent free by using IoT monitored sensors. Rodents are especially dangerous to be found lurking in restaurants because they carry diseases and can cause electrical fires. Devices can be placed throughout the restaurant to detect any motion that occurs. When the devices detect a motion, restaurant owners will receive notifications and will be immediately aware of any rodents that may have snuck into the restaurant.
These sensors give restaurant owners a chance to proactively address a rodent issue before it causes damage to their business.
In addition to monitoring sanitation and detecting motion, restaurant owners can leverage the IoT many other ways. For example, IoT devices can be placed on trash bins to alert when they are full and ready to be taken out. They can also be placed near pipes to detect a leak. Sensors can also be placed on all refrigerators to detect temperature. With accurate updates on refrigerators’ temperatures, restaurant owners can easily monitor and ensure that food is stored at the appropriate temperature around the clock—and be immediately alerted if a power issue causes temperatures to change.
IoT devices can offer restaurant owners insights to help them change their operations and behavior for the better. While everyone is eager to go back to “normal” and want our favorite restaurants to re-open as soon as possible, it is important that restaurant owners have the tools needed to reopen safely—and create efficiencies that can help recoup lost income due to COVID-19 restrictions. Restaurant owners looking to receive real-time, accurate data and insights to help run their restaurants more efficiently and ensure a safe and comfortable experience for customers can turn to the IoT to achieve their goals.
This year’s GFSI Conference will take place March 23–25 and bring together experts, decision makers and innovators in the food industry. With the theme of “rethink, reset, recharge”, the three-day virtual program includes online networking features to allow attendees to connect with professionals across the globe, and sessions that explore COVID-19; supply chain disruption and public health; building consumer trust and transparency; sharing best practices; and technologies shaping the future of food safety.
“Collaboration to ensure safe food for consumers everywhere and sustainable food systems has never been more critical – and this event provides a major opportunity to learn from an unprecedented period and move forwards in the best possible way. We’re excited by the chance to help colleagues across the industry build on the ingenuity, resilience and dedication shown by the food industry over the past 12 months,” said Erica Sheward, director of GFSI, in a press release. “With the conference taking place virtually for the first time, it’s easier than ever before for food industry professionals to get involved—and we’re urging people from all corners of the globe to ensure they’re part of this unique and collaborative forum. Food safety is everyone’s business, and we must continue to work together to build consumers’ trust in the food they buy.”
More information about the GFSI conference, along with registration, agenda and partner details, can be found on the event website.
COVID-19 has been a sharp wake-up call for many food manufacturers in the need for resilient production environments that can readily respond to large and sudden changes, including fluctuations in demand and disruptive external events. This means being able to optimize operations for the following:
Efficiency: Where you can achieve constant output even when given fewer inputs—such as in workforce availability or resources. This was especially important when the pandemic caused widespread supply shortages, as well as staffing shortages due to social distancing measures.
Productivity: When you can ensure that, given the amount of available input (i.e., raw ingredients, manpower, equipment availability), you can maintain a consistent output to meet demand in the marketplace.
Flexibility: Where you can rapidly and intelligently adapt your processes in the face of change, in ways that are in the best interest of your business, the supply chain, and the consumers who purchase and trust in your products.
That trust is paramount, as manufacturers must continue to uphold quality and safety standards—especially during a time when public health is of the upmost importance. But between operational challenges and managing product quality, that’s a lot for manufacturers to wade through during a crisis.
To navigate the current COVID reality and improve response to future events, more organizations are looking to harness the power of data to enable agile decision-making and, in turn, build more resilient production environments.
Harnessing the Power of Data
The key to harnessing data for agile decisions is to aggregate end-to-end process information and make it available in real time. When you can achieve that, it’s possible to run analytics and derive timely insights into every facet of production. Those insights can be used to increase efficiency, productivity and flexibility—as well as ensure product quality and safety—even amidst upheaval.
When looking at solutions to aggregate data from a single site—or better yet, multiple sites—all roads lead to the cloud. Namely, cloud-based quality intelligence solutions can decouple the data from physical locations—such as paper checklists, forms, or supervisory control and data acquisition (SCADA) and human-machine interfaces (HMI) systems—and centralize what’s collected digitally in a unified repository. The data can then be accessed, analyzed, and consumed by those who need actionable insights from anywhere, at any time, and on any device, making cloud an ideal solution for connecting on-site operators and remote employees.
An Opportunity for Broader Transformation
In migrating to the cloud, manufacturers open the opportunity to break away from the legacy, manual processes of yesterday and transition to more nimble, digitally enabled environments of tomorrow. For example, manual processes are often highly dependent on individual operator knowledge, experience and judgement. As the pandemic has shown, such institutional knowledge can be lost when employees become ill, or are unavailable due to self-isolation or travel restrictions, presenting a risk to operational efficiency and productivity. But if that valuable institutional knowledge were captured and codified in a quality intelligence solution as predefined workflows and prescriptive instructions, then a manufacturer could more easily move their resources and personnel around as necessary and find comfort knowing that processes will be executed according to best practices.
For many organizations, this would be a remarkable transformation in the ways of working, where data and digital technologies can augment human capacity and flexibility. Take for instance, in traditional production environments, a lot of human effort is spent on monitoring lines to catch process deviations or events like machine anomalies or quality issues. Using real-time data, next-generation solutions can take on that burden and continuously monitor what’s happening on the plant floor—only alerting relevant teams when an issue arises and they need to intervene. Manufacturers can thereby redeploy people to other tasks, while minimizing the amount of resources necessary to manage product quality and safety during daily production and in the event of disruption.
Ensuring Quality Upstream and Downstream
One company that has succeeded in digital transformation is King & Prince, a manufacturer of breaded, battered and seasoned seafood. When the company digitized its manufacturing processes, it centralized the quality data from all points of origin in a single database. The resulting real-time visibility enables King & Prince to monitor quality on more than 100 processes across three U.S. plants, as well as throughout a widespread network of global suppliers.
With this type of real-time visibility, a company can work with suppliers to correct any quality issues before raw materials are shipped to the United States, which directly translates to a better final product. This insight also helps plant-based procurement managers determine which suppliers to use. Within its own plants, operators receive alerts during production if there are any variations in the data that may indicate inconsistencies. They can thereby stop the process, make necessary adjustments, and use the data again to confirm when everything is back on track.
During finished product inspections, the company can also review the captured data to determine if they need to finetune any processes upstream and respond sooner to prevent issues from making it downstream to the consumer level. Overall, the company is able to better uphold its quality and safety standards, with the number of customer complaints regarding its seafood products dropping to less than one per million pounds sold year over year—and that’s all thanks to the harnessing of data in a digitally enabled production environment.
There’s No Time Like the Present
In truth, technologies like the cloud and quality intelligence solutions, and even the concept of digital transformation, aren’t new. They’ve been on many company agendas for some time, but just haven’t been a high priority. But when the pandemic hit, organizations were suddenly faced with the vulnerabilities of their long-held operational processes and legacy technologies. Now, with the urgency surrounding the need for resilient production environments, these same companies are thinking about how to tactically achieve digital transformation in the span of a few weeks or months rather than years.
Yet while digital transformation may sound like a tremendous initiative with high risks and expenses, it’s more tangible than some may think. For example, cloud-based Software-as-a-Service (SaaS) solutions offer flexible subscription-based models that keep costs low on top of rapid scalability. Digital transformation doesn’t have to be an all-or-nothing endeavor either. In fact, it can be better to progress incrementally, starting first with the manufacturing areas that are most in need or have the most issues. This minimizes unnecessary risk, makes digital transformation more achievable and realistic over short timeframes, and avoids overwhelming already maxed out operational and IT teams.
All things must pass. The pandemic will eventually be over. But in its wake will be a permanent legacy on not just society, but also on the manufacturing sector. In my opinion, digital transformation is a fundamental basis for building resilience into the modern food production environment. Now, more than ever, is the time to address that opportunity head on.
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